Bert2bert Shared German Finetuned Summarization

🚀 German BERT2BERT fine-tuned on MLSUM DE for summarization

This project presents a German BERT2BERT model fine-tuned on the MLSUM DE dataset for text summarization. It offers an effective solution for summarizing German news articles.

🚀 Quick Start

Prerequisites

• Python
• PyTorch
• Transformers library

Installation

Ensure you have the necessary libraries installed. You can install them using pip:

pip install torch transformers

Usage

Here is a basic example of how to use the model for summarization:

import torch
from transformers import BertTokenizerFast, EncoderDecoderModel
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bert2bert_shared-german-finetuned-summarization'
tokenizer = BertTokenizerFast.from_pretrained(ckpt)
model = EncoderDecoderModel.from_pretrained(ckpt).to(device)

def generate_summary(text):
    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
    input_ids = inputs.input_ids.to(device)
    attention_mask = inputs.attention_mask.to(device)
    output = model.generate(input_ids, attention_mask=attention_mask)
    return tokenizer.decode(output[0], skip_special_tokens=True)

text = "Your text here..."

generate_summary(text)

✨ Features

• Fine-tuned on MLSUM DE: The model is fine-tuned on the MLSUM DE dataset, which is suitable for German news summarization.
• Based on BERT2BERT: Leveraging the power of BERT2BERT architecture for effective summarization.

📦 Model

• Base Model: bert-base-german-cased (BERT Checkpoint)

📦 Dataset

MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. MLSUM de

📊 Results

📄 Acknowledgments

Created by Manuel Romero/@mrm8488 with the support of Narrativa Made with ♥ in Spain